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https://issues.apache.org/jira/browse/CASSANDRA-2699?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13583453#comment-13583453
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Benjamin Coverston edited comment on CASSANDRA-2699 at 2/21/13 7:19 PM:
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>From CASSANDRA-4482
{quote}
Instead, we maintain a Merkle Tree (MT) in memory and update it with every
single column insert in ColumnFamilyStore.apply(). We use
column.updateDigest(digest) on all the changes in order to create a hash per
column update and then XOR this hash with the existing one in the Merkle Tree
bucket for the corresponding row.
This Merkle Tree is created with the column family (one per range), initialized
with zeros, and persisted to disk with regular snapshots.
The commutative properties of XOR make it possible to update the MT
incrementally without having to read on write.
{quote}
I'm pretty sure this means that we should be able to XOR the buckets together
from pre-computed merkle tree SSTable components.
We could just create these on flush, merge them on compaction, then validation
compaction is just a read of MT components and merge.
was (Author: bcoverston):
From CASSANDRA-4482
{quote}
Instead, we maintain a Merkle Tree (MT) in memory and update it with every
single column insert in ColumnFamilyStore.apply(). We use
column.updateDigest(digest) on all the changes in order to create a hash per
column update and then XOR this hash with the existing one in the Merkle Tree
bucket for the corresponding row.
This Merkle Tree is created with the column family (one per range), initialized
with zeros, and persisted to disk with regular snapshots.
The commutative properties of XOR make it possible to update the MT
incrementally without having to read on write.
{quote}
I'm pretty sure this means that we should be able to XOR the buckets together
from pre-computed merkle tree SSTable components.
We could just create these on flush, merge them on compaction, then validation
compaction is just a read, MT components and merge.
> continuous incremental anti-entropy
> -----------------------------------
>
> Key: CASSANDRA-2699
> URL: https://issues.apache.org/jira/browse/CASSANDRA-2699
> Project: Cassandra
> Issue Type: Improvement
> Reporter: Peter Schuller
> Assignee: Peter Schuller
>
> Currently, repair works by periodically running "bulk" jobs that (1)
> performs a validating compaction building up an in-memory merkle tree,
> and (2) streaming ring segments as needed according to differences
> indicated by the merkle tree.
> There are some disadvantages to this approach:
> * There is a trade-off between memory usage and the precision of the
> merkle tree. Less precision means more data streamed relative to
> what is strictly required.
> * Repair is a periodic "bulk" process that runs for a significant
> period and, although possibly rate limited as compaction (if 0.8 or
> backported throttling patch applied), is a divergence in terms of
> performance characteristics from "normal" operation of the cluster.
> * The impact of imprecision can be huge on a workload dominated by I/O
> and with cache locality being critical, since you will suddenly
> transfers lots of data to the target node.
> I propose a more incremental process whereby anti-entropy is
> incremental and continuous over time. In order to avoid being
> seek-bound one still wants to do work in some form of bursty fashion,
> but the amount of data processed at a time could be sufficiently small
> that the impact on the cluster feels a lot more continuous, and that
> the page cache allows us to avoid re-reading differing data twice.
> Consider a process whereby a node is constantly performing a per-CF
> repair operation for each CF. The current state of the repair process
> is defined by:
> * A starting timestamp of the current iteration through the token
> range the node is responsible for.
> * A "finger" indicating the current position along the token ring to
> which iteration has completed.
> This information, other than being in-memory, could periodically (every
> few minutes or something) be stored persistently on disk.
> The finger advances by the node selecting the next small "bit" of the
> ring and doing whatever merkling/hashing/checksumming is necessary on
> that small part, and then asking neighbors to do the same, and
> arranging for neighbors to send the node data for mismatching
> ranges. The data would be sent either by way of mutations like with
> read repair, or by streaming sstables. But it would be small amounts
> of data that will act roughly the same as regular writes for the
> perspective of compaction.
> Some nice properties of this approach:
> * It's "always on"; no periodic sudden effects on cluster performance.
> * Restarting nodes never cancels or breaks anti-entropy.
> * Huge compactions of entire CF:s never clog up the compaction queue
> (not necessarily a non-issue even with concurrent compactions in
> 0.8).
> * Because we're always operating on small chunks, there is never the
> same kind of trade-off for memory use. A merkel tree or similar
> could be calculated at a very detailed level potentially. Although
> the precision from the perspective of reading from disk would likely
> not matter much if we are in page cache anyway, very high precision
> could be *very* useful when doing anti-entropy across data centers
> on slow links.
> There are devils in details, like how to select an appropriate ring
> segment given that you don't have knowledge of the data density on
> other nodes. But I feel that the overall idea/process seems very
> promising.
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